A fast taboo search algorithm for the job shop problem
Management Science
An Efficient Genetic Algorithm for Job Shop Scheduling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Scheduling by Genetic Local Search with Multi-Step Crossover
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parallel GRASP with path-relinking for job shop scheduling
Parallel Computing - Special issue: Parallel computing in numerical optimization
New EAX crossover for large TSP instances
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Analysis of the performance of genetic multi-step search in interpolation and extrapolation domain
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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The deterministic Multi-step Crossover Fusion (dMSXF) is an improved crossover method of MSXF which is a promising method of JSP, and it shows high availability in TSP. Both of these crossover methods introduce a neighborhood structure and distance in each permutation problem and perform multi-step searches in the interpolation domain focusing on inheritance of parents' characteristic. They cannot work effectively when parents stand close each other since they search in interpolation domain. Therefore in the case of the MSXF, the Multi-step Mutation Fusion (MSMF), which is the multi-step search in the extrapolation domain, is combined as the supplementary search to improve its search performance. On the other hand, the search mechanism for acquisition of characteristics, such as MSMF, is not applied to dMSXF. In this paper, we introduce a deterministic MSMF (dMSMF) mechanism as complementary multi-step extrapolation search. We apply dMSXF+dMSMF to TSP and JSP, which have structural difference between their landscapes. Through the experiments it was shown that the deterministic multi-step search in interpolation/extrapolation domain performed effectively in combinatorial problems.